In order to make effective use of a three-way catalyst used to process CO, HC and NOx, which are toxic substances present in engine exhaust gases, the engine must be operated at the theoretical AFR.
In engines using a three-way catalyst to process exhaust gases, an O.sub.2 sensor installed in the exhaust manifold is used to detect whether the combustion is on the rich or on the lean side, and the AFR is feedback-controlled to a theoretical AFR by adjusting the fuel supplied by a fuel injector based on the detected value.
However it is difficult to ensure sufficient response capacity from this kind of feedback control.
Tokkai Sho 60-145443, 63-41635, 63-38656 and Tokkai Hei 1-138345 published by Japanese Patent Office therefore disclose methods of improving the response and control precision by learning during a sampling period under a variety of different conditions, and applying correction values based on these learned values to control the AFR.
This system is applied to fuel injection devices of the L-Jetronic type, wherein the injection pulse width TI corresponding to the amount of fuel required in one ignition cycle is given by the following relation: EQU TI=Tp.times.Co.times..alpha..times..alpha.m+Ts
where,
Tp is a basic pulse width of a fuel injection, EQU Tp=K.times.Qa/N PA1 K is a constant, Qa is intake volume and N is engine speed. PA1 .alpha. is an AFR feedback control coefficient calculated according to the deviation between the real mixing ratio and a predetermined target ratio. The mixing ratios are calculated from the AFR by the equation: EQU Real mixing ratio=theoretical air-fuel ratio/real air-fuel ratio EQU Target mixing ratio=theoretical air-fuel ratio/target air-fuel ratio PA1 Co are various correction coefficients to improve specific running conditions of the engine. PA1 Ts is an ineffectual pulse width.
Here, .alpha.m is an AFR learning correction coefficient introduced for the purpose of improving the response of the AFR correction. These parameters may be represented by a learning area for storage of AFR coefficients .alpha.m. This learning area is divided into a plurality of small areas with Tp and N as coordinates, and .alpha.m is updated in each small area.
In one small area, for example, when a certain set of predetermined conditions are satisfied, (e.g. the AFR feedback signal is sampled a certain number of times during feedback control), updated learned values are calculated from an intermediate value of .alpha. computed from the AFR sensor output and a learned value which previously occupied this small area, and the result of this calculation is stored in the same area.
This type of learning control finds a mixing ratio error area during an acceleration judgment period (sampling period), and performs learning such that the error area is 0.
The mixing ratio error is a value obtained by subtracting the target mixing ratio from the real mixing ratio. If the mixing ratio error area is negative, for example, the real mixing ratio is too lean, so transient learned values are updated to make it richer. This type of learning control is effective for improving exhaust emissions, and as the AFR is particularly liable to fluctuate during transient running conditions, learning is very much required at these times.
There are however the following problems in performing this type of learning under transient running conditions.
Firstly, when a steady state error exists at the end of a sampling period (end of acceleration) due to a performance scattering of deterioration of the fuel injectors and air flow meters, learning precision declines if this error is incorporated in the errors occurring under transient conditions.
Further, if learning is applied only to the error area, sudden displacements of the AFR to the lean or rich side responsible for the hesitation or stumbling of the engine that tends to occur in transient conditions cannot effectively be suppressed.